IoT – Blog by Neha Mendon

A Primer to Smart Manufacturing and Industry 4.0

The manufacturing industry is leading the way in digital transformation by utilizing technologies such as big data analytics, artificial intelligence (AI), and robotics. Research has shown significant benefits from these advancements, with machine downtime potentially reduced by 30% to 50% and quality-related costs cut by 10% to 20%.

AI is revolutionizing the manufacturing sector by improving efficiency, precision, and adaptability across various production processes, especially in the context of Industry 4.0. It can analyse large amounts of data from sensors, equipment, and production lines to optimize efficiency, enhance quality, and minimize downtime. By using algorithms to identify patterns in data, AI can predict potential issues, recommend improvements, and even adjust processes autonomously in real-time.

In today’s landscape, digital transformation is essential for business survival. From automated factories to AI-driven quality control, the primary goal of digital transformation is to gain a competitive edge through technology, leading to improved customer experiences and lower operational costs.

Latest Trends on Smart Manufacturing and Industry 4.0

AI in manufacturing goes beyond automation by facilitating real-time decision-making. This function is part of what is often referred to as “smart factories” or “smart manufacturing¹,” both of which are key components of Industry 4.0².

Smart manufacturing, as a facet of the digital transformation under Industry 4.0, employs a combination of emerging technologies and diagnostic tools—such as artificial intelligence (AI) applications, the Internet of Things (IoT), robotics, and augmented reality—to optimize enterprise resource planning (ERP). This makes companies more agile and adaptable.

One of the most significant advantages of AI technology in smart manufacturing is its capability for efficient real-time data analysis. With IoT devices and sensors collecting data from machines, equipment, and assembly lines, AI-powered algorithms can quickly process and analyze this information to identify patterns and trends. This helps manufacturers gain insights into the performance of their production processes.

Benefits of Artificial Intelligence in Manufacturing

Increased Efficiency and Productivity: AI systems can automate many routine and repetitive tasks, allowing employees to focus on more complex and value-added activities. This shift can lead to increased efficiency and productivity in manufacturing processes. AI-driven automation accelerates production by taking over repetitive tasks, reducing human error, and optimizing workflows. With integrated systems, processes become more streamlined—from raw materials to finished products—minimizing manual intervention and enabling “hands-off” manufacturing.

Improved Decision-Making: AI processes data in real-time, empowering managers to make informed, data-driven decisions. Digital twins allow manufacturers to simulate production scenarios, minimizing risks and enhancing decision-making by testing outcomes before full implementation.

Innovation and Competitive Advantage: With faster prototyping, generative design, and digital twin simulations, AI enables manufacturers to innovate quickly and efficiently. By reducing time-to-market and supporting more advanced product designs, AI helps companies remain competitive and responsive in a rapidly evolving industry landscape.

Cost Reduction: AI improves cost-effectiveness through more than just automation. Digital twin technology and AI-driven predictive maintenance can extend the life of equipment, leading to long-term savings, as well as conserving energy, time, water, and other resources. Additionally, optimized supply chain management through AI-assisted data analysis enhances demand planning and inventory management, making them more cost-efficient and resilient to risks. Automation, predictive analytics, and improved quality control all contribute to significant cost savings. AI reduces labor and maintenance expenses, lowers waste, and optimizes energy consumption, creating a leaner, more cost-effective production environment.

Environmental Sustainability: By optimizing the management of resources, logistics, and warehouses, AI helps manufacturers reduce energy and material waste, thereby lessening their ecological footprint. This positive impact is crucial for sustainable manufacturing, which integrates product and process design with manufacturing planning and control to identify, quantify, assess, and manage the flow of environmental waste.

AI’s ability to optimize resource allocation, minimize energy use, and limit waste supports environmentally friendly manufacturing practices. Components with self-monitoring sensors also help reduce maintenance needs, contributing to a lower environmental impact.

Industry Applications of AI in Manufacturing

Factory In a Box

The “factory in a box” concept involves modular, self-contained manufacturing units that can be quickly deployed to various locations. These units are equipped with AI-driven automation, Internet of Things (IoT) sensors, and real-time data analytics, enabling flexible, localized production.

This approach allows companies to bring manufacturing closer to demand, reduce logistics costs, and swiftly respond to changing needs. Currently, industries such as electronics, automotive, and pharmaceuticals are experimenting with these portable units.

The full potential of this concept will be realized through future advancements in automation, modular design, and data integration, making it a scalable and mainstream solution. This attractiveness of the “factory in a box” approach encourages more enterprises, particularly small and medium-sized enterprises (SMEs), to adopt an end-to-end packaged process where software works seamlessly with tooling. The integration of sensors and analytics enhances efficiency. Additionally, the inclusion of digital twin technology enables engineers to simulate new manufacturing processes, reducing risk in decision-making.

Digital Twin Technology

AI is utilized to create virtual replicas of processes, production lines, factories, and supply chains. These digital twins simulate, analyze, and predict performance in real-time. By digitally mirroring the real world, digital twins enable manufacturers to monitor and optimize operations without needing to intervene directly in the physical asset.

Digital twins rely on data from Internet of Things (IoT) sensors, programmable logic controllers (PLCs), deep learning, and AI algorithms. These technologies continuously update the digital model with live data, providing an accurate and current virtual representation.

A digital twin is a virtual representation of a real-world product or asset. By integrating AI techniques with digital twins, manufacturers can enhance their understanding of products and explore future actions that may improve asset performance. There are typically four applications of digital twins in manufacturing, including predictive maintenance, shop floor performance improvement, self-driving car developments, and design customization.

Predictive Maintenance

A major focus for AI in manufacturing is predictive maintenance. This technology enables engineers to equip factory machines with pre-trained AI models that incorporate accumulated knowledge about the equipment. By analysing data from the machinery, these models can recognise new patterns of cause and effect that emerge on-site, helping to prevent issues before they occur.

AI examines sensor data from machines to predict failures in advance. By utilizing a digital twin to assess patterns in equipment behaviour and performance, these systems can notify operators about potential problems early on.

This proactive approach allows companies to avoid breakdowns before they escalate. For instance, automobile manufacturers often use predictive maintenance for assembly-line robots, which significantly reduces unplanned downtime and leads to substantial cost savings.

Additionally, this strategy enables manufacturing companies to schedule maintenance during non-peak hours, minimizing disruptions to production schedules. In industries like automotive and electronics manufacturing, precision is crucial due to the complexity of the components involved. AI plays a vital role in reducing production errors, improving product designs, and accelerating time-to-market.

Smart Factory

The increasing use of cameras, sensors, and other technologies that monitor manufacturing processes 24/7—originally initiated by smart factories and Industry 4.0 initiatives—enables manufacturers to provide AI with vast amounts of real-time data. This continuous data flow enhances the value that manufacturers can derive from their data and supports various applications of AI. Notably, several key applications of artificial intelligence in manufacturing, such as predictive maintenance, digital twin technology, and AI-assisted visual inspection, rely heavily on this data.

Another emerging trend is the use of smart components, which are parts equipped with embedded sensors. These include devices, machines, robots, or any objects with network-connected sensors that collect and upload data for analysis. Many of these sensors function as edge computing devices, featuring low-cost processors that perform initial computing tasks locally. This local processing enables preliminary data analysis or filtering at the source before the data is transmitted to the cloud, thus enhancing efficiency in data handling and transmission.

Generative AI

Product and Spare Parts Search: Generative AI assists customers in finding products even when they are unsure of the exact names or codes. Customers can describe the features they want, and AI translates these descriptions into effective search queries. Additionally, it can generate detailed product descriptions, improving search accuracy through a better understanding of the semantics involved.

Document Search and Summarization: Generative AI is revolutionizing document management in manufacturing by enabling efficient search and summarization. Rather than manually sorting through technical drawings, reports, and records, AI can process large volumes of documents to identify patterns and summarize key information. This technique accelerates retrieval and presents complex information in clear and accessible formats.

Manufacturing-Adjacent Areas: Generative AI is also beneficial in areas that support the manufacturing process, including ticket handling, call handling, market research, and the creation of product descriptions, maintenance schedules, and instructions.

Custom Manufacturing: AI allows manufacturers to offer mass customization, enabling products to be tailored to individual customer preferences without slowing down production. By integrating AI into the design process, companies can quickly adapt designs based on real-time consumer feedback. For example, clothing manufacturers utilize AI algorithms to personalize products, allowing customers to select designs that match their specific tastes. This flexibility enhances customer engagement and satisfaction.

Wrapping Up

A recent survey reveals that 63% of manufacturers plan to increase their spending on AI. The efficiency gains from integrating AI lead to significant cost and time savings, allowing resources to be redirected to more critical tasks and opportunities.

This insight aims to understand how manufacturers are benefiting from AI use cases, particularly in engineering, design, and factory operations. Many executives who responded to the survey indicate that they intend to significantly boost AI spending over the next two years. Those who have not yet implemented AI in production are progressing gradually. To develop and scale use cases effectively, these manufacturers must address challenges related to talent, skills, and data.

Talent, skills, and data are the primary constraints on scaling AI. In both engineering, design, and factory operations, manufacturers report that a lack of talent and skills is their biggest challenge in scaling AI use cases. This issue becomes more pronounced as these use cases move closer to production. Many respondents also indicate that inadequate data quality and governance hinder use-case development. Additionally, insufficient access to cloud-based computing power is frequently mentioned as a constraint in engineering and design.

The desired gains from AI are specific to various manufacturing functions. The most common use cases deployed by manufacturers involve product design, conversational AI, and content creation. Knowledge management and quality control are often cited as being in the pilot stage. In engineering and design, manufacturers primarily seek improvements in speed, efficiency, reduced failures, and enhanced security. In the factory, the top priorities are better innovation, improved safety, and a reduced carbon footprint.

To scale AI effectively, fragmentation must be addressed. Most manufacturers find that some modernization of data architecture, infrastructure, and processes is essential to support AI, along with other technology and business priorities. A modernization strategy that enhances the interoperability of data systems between engineering, design, and the factory, as well as between operational technology (OT) and information technology (IT), should be a key focus.

Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Artificial Intelligence in Smart Food Supply Chains

21 Nov 20246 Nov 2025 Nehatrendy

A Taster to Smart Food Supply Chains

Have you wondered how global food supply chains have become efficient and resilient over the years? It’s because of modern technologies such as Artificial Intelligence (AI) which plays a crucial role in nourishing the world’s growing population. AI optimizes logistics, and reduces waste and costs, while predictive analytics help forecast demand and respond to disruptions quickly. By leveraging data-driven insights, AI promotes sustainability and ensures a reliable food supply. It benefits all stakeholders in the value chain such as producers, distributors, retailers, and consumers, creating a more agile and responsive ecosystem.

This is where the concept of Smart Food Supply Chains comes into play, utilizing a blend of modern technologies such as Artificial Intelligence (AI) to address challenges within food supply chains. Smart Food Supply Chains integrate technology and data to improve efficiency, traceability, and responsiveness from farm to table. Their goals include minimizing waste, enhancing sustainability, and ensuring food safety.

Key objectives by leveraging these technologies involve real-time monitoring, predictive analytics, and agile logistics to meet consumer needs while optimizing resource use throughout the supply chain.

In a world of constant change, Artificial Intelligence (AI) optimizes all dimensions of the global supply chain such as production, distribution, and inventory management, lowering waste and costs. Furthermore, it strengthens supply chains against disruptions, ensuring food security, sustainability, and a more environmentally friendly global food distribution system.

Demand for Smart Food Supply Chains

Globalization and interconnected supply chains have led to complex disruptions in global value chains, caused by various factors including natural disasters, climate events, geopolitical conflicts, and economic crises. Recent advancements in AI, offer new opportunities for effectively forecasting and managing these disruptions.

These technologies allow organizations to effectively manage disruptions like COVID-19 on food supply chains, achieving food security in the face of climate change, leveraging predictive models in agriculture, and assessing how disruptions affect food price volatility. These insights offer valuable guidance for policymakers and researchers seeking to enhance food security amid uncertainties and disruptions in global value chains.

Food supply chains have traditionally relied on manual processes and outdated technologies, resulting in inefficiencies at various stages—including manufacturing, distribution, and stock management. These inefficiencies lead to increased costs and longer lead times, causing delays in delivering products to consumers. In today’s fast-paced world, these shortcomings can hinder a supply chain’s ability to meet consumer demands promptly.

Additionally, traditional supply chains are often plagued by significant food waste and spoilage. Insufficient monitoring and handling practices result in food products expiring before reaching consumers, leading to financial losses and contributing to the global food waste crisis. This situation has financial repercussions and poses environmental challenges, as resources used in food production are wasted. Addressing these issues is essential for developing more sustainable and environmentally friendly supply chains. Artificial Intelligence (AI) is increasingly being utilized to tackle these problems and optimize supply chains.

Digitization of Food Supply Chains

Supply Chain Management (SCM) involves the flow of goods, services, and information from points of origin to customers through a network of interconnected entities and activities. In typical SCM scenarios, it is assumed that factors such as capacity, demand, and cost are known. However, this assumption often does not hold in reality, as uncertainties arise from variations in customer demand, transportation issues, organizational risks, and lead times. Demand uncertainties, in particular, significantly impact supply chain performance, affecting production scheduling, inventory planning, and transportation.

In this context, demand forecasting becomes a crucial strategy to address the uncertainties faced in supply chains. A variety of statistical analysis techniques have been employed for demand forecasting in SCM, including time-series analysis and regression analysis. Traditional methods, such as time series analysis and regression models, are commonly employed to identify trends and seasonality in demand data. Additionally, advanced algorithms like random forests and neural networks can analyze complex datasets to uncover patterns that traditional methods overlook. These models can also adapt to changes in demand dynamics over time.

Recently, advancements in technologies like AI and predictive analytics have emerged as effective tools for generating more accurate predictions. These technologies enhance our understanding of customer needs, facilitate the evaluation of supply chain performance, improve efficiency, reduce response times, and support risk management in supply chains. The increasing need for customer behavior analysis and demand forecasting is driven by globalization, heightened market competition, and the rise of digitization practices in supply chains.

A Data-Driven Approach for Smart Food Supply Chains

Supply chain data is generated in high volumes across numerous points and serves various purposes, reflecting the complexity of interactions among multiple suppliers, products, and customers. This data is high-dimensional and processes many transactions continuously within supply chain networks. Given such complexities, there has been a shift away from conventional demand forecasting methods that rely on identifying statistically meaningful trends from historical data.

Instead, we are moving towards intelligent forecasting systems that learn from historical data and can adapt to predict the constantly changing demands in supply chains. This capability relies on AI, machine learning, and predictive analytics techniques that uncover forecasting rules by identifying the underlying relationships among demand data. By utilizing historical data and advanced statistical methods, organizations can gain insights into future demand patterns, which is essential for effective inventory management and resource allocation.

To maximize the benefits of Artificial Intelligence, it is crucial to integrate intelligent forecasting models with supply chain operations. Incorporating real-time data from IoT devices can enhance the accuracy of forecasts by providing up-to-date information on inventory levels and market conditions. Effective collaboration among all departments—including sales, marketing, and supply chain teams—ensures that these forecasts align with business objectives and market realities.

Leveraging Artificial Intelligence for demand forecasting not only enhances operational efficiency but also supports strategic decision-making. By adopting a data-driven approach, organizations can better navigate the complexities of supply chain management and respond proactively to changing market demands.

Benefits of Smart Food Supply Chain

The need for efficiency and resilience in food supply chains is paramount. Efficiency ensures that resources are used optimally at every stage of the supply chain, minimizing waste and lowering costs. In contrast, resilience is essential for facing the multitude of challenges that food supply chains encounter, such as natural disasters, pandemics, and geopolitical disruptions.

Enhanced Efficiency: A combination of AI and IoT devices tracks food items in real-time, reducing waste and improving delivery efficiency. By optimizing processes like stock management and logistics, AI significantly enhances the effectiveness of smart food supply chains. AI-powered sensors and monitoring systems track food products from farm to table, providing real-time information about their location, condition, and quality.
Improved Resilience: AI predicts and mitigates supply chain disruptions, reducing food waste and ensuring a more robust supply system even in challenging situations. It strengthens resilience in smart food supply chains by anticipating and addressing potential disruptions, such as supply chain bottlenecks or disasters. Additionally, AI improves stock management and spoilage prediction, helping to further reduce food waste.
Sustainability and Environmental Impact: AI minimizes the carbon footprint by optimizing transportation and energy usage, contributing to more sustainable and eco-friendly food supply systems. In smart food supply chains, AI promotes sustainability through environmentally friendly practices, aiding in the reduction of carbon emissions linked to transportation and energy consumption.

Industry Applications of Smart Food Supply Chains

Food supply chains are becoming increasingly digitized and automated through the use of modern technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain. These digitization efforts often depend on cloud computing, which can create challenges related to bandwidth overhead, high latency, and issues of security and privacy.

In contrast, Edge AI can enhance scalability and mitigate the security and privacy concerns related to centralized cloud computing. This shift aligns with the provenance and traceability requirements of food supply chains, while also supporting their digitization through these modern technologies. This proposed Edge AI approach to supply chain digitization paves the way for greater resilience in modern digital supply chains.

When used alongside AI, IoT plays a crucial role in smart food supply chains. It enables real-time monitoring and management by collecting information on temperature, humidity, and location. This data allows for effective management of stock and transportation, reducing spoilage and waste. Furthermore, AI coupled with Blockchain and IoT technologies improves supply chain transparency by enabling stakeholders and customers to trace a product’s journey from its source to its destination. This transparency not only ensures food safety and quality but also fosters customer trust in the supply chain, contributing to a more sustainable and responsible food distribution system.

Conclusion

AI is revolutionizing food supply chains, presenting significant opportunities for efficiency and resilience. It optimizes production, distribution, and inventory management, benefiting producers, distributors, retailers, and consumers. AI enhances traceability, transparency, and sustainability, promoting ethical and eco-friendly practices.

Despite the many advantages of digitizing the food supply chain, there are growing ethical concerns surrounding the use of AI in agriculture. These concerns include enforcing sustainable farming methods and promoting equitable access to AI technologies. Ethical AI emphasizes fairness, transparency, and environmental responsibility, ensuring that advancements in agricultural technology benefit all stakeholders while adhering to moral standards in food production.

To create an environmentally friendly and resilient future, stakeholders must prioritize AI integration. Governments should establish supportive regulatory frameworks, and industries should adopt practices that align with ethical and sustainable values. Additionally, funding for worker retraining and upskilling is essential to address concerns about job displacement.

The message is clear: Embrace Edge AI as a transformative force in food supply chains, ensuring that it serves not only economic interests but also ethical, environmental, and social objectives.

Furthermore, the food supply chain consumes substantial amounts of energy, significantly impacting the environment. AI-driven optimization can reduce energy consumption by facilitating information sharing, minimizing energy use, optimizing transportation routes, and lowering greenhouse gas emissions.

Tag: IoT

Transforming Smart Manufacturing and Industry 4.0